Forests and floods: A new paradigm sheds light on age‐old controversies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The science of forests and floods is embroiled in conflict and is in urgent need of reevaluation in light of changing climates, insect epidemics, logging, and deforestation worldwide. Here we show how an inappropriate pairing of floods by meteorological input in analysis of covariance (ANCOVA) and analysis of variance (ANOVA), statistical tests used extensively for evaluating the effects of forest harvesting on floods smaller and larger than an average event, leads to incorrect estimates of changes in flood magnitude because neither the tests nor the pairing account for changes in flood frequency. We also illustrate how ANCOVA and ANOVA, originally designed for detecting changes in means, do not account for any forest harvesting induced change in variance and its critical effects on the frequency and magnitude of larger floods. The outcomes of numerous studies, which applied ANCOVA and ANOVA inappropriately, are based on logical fallacies and have contributed to an ever widening disparity between science, public perception, and often land‐management policies for decades. We demonstrate how only an approach that pairs floods by similar frequency, well established in other disciplines, can evaluate the effects of forest harvesting on the inextricably linked magnitude and frequency of floods. We call for a reevaluation of past studies and the century‐old, preconceived, and indefensible paradigm that shaped our scientific perception of the relation between forests, floods, and the biophysical environment.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it